An Improved Lithium-Ion Battery Fire and Smoke Detection Method Based on the YOLOv8 Algorithm

This paper introduces a novel algorithm—YOLOv8 (You Only Look Once version 8) + FRMHead (a multi-branch feature refinement head) + Slimneck (a lightweight bottleneck module), abbreviated as YFSNet—for lithium-ion battery fire and smoke detection in complex backgrounds. By integrating advanced modules for richer feature extraction and streamlined architecture, YFSNet significantly enhances detection precision and real-time performance. A dataset of 2300 high-quality images was constructed for training and validation, and experimental results demonstrate that YFSNet boosts detection precision from 95.6% in the traditional YOLOv8n model to 99.6%, while the inference speed shows a marked improvement with FPS increasing from 49.75 to 116.28. Although the recall rate experienced a slight drop from 97.7% to 93.1%, the overall performance in terms of F1-score and detection accuracy remains robust, underscoring the method’s practical value for reliable and efficient battery fire detection in fire safety systems.